Predicting students’ performance at higher education institutions using a machine learning approach

Abstract

Purpose

Many studies have examined pre-diploma students' backgrounds and academic performance with results showing that some did not achieve the expected level of competence. This study aims to examine the relationship between students’ demographic characteristics and their academic achievement at the pre-diploma level using machine learning.

Design/methodology/approach

Secondary data analysis was used in this study, which involved collecting information about 1,052 pre-diploma students enrolled at Universiti Teknologi MARA (UiTM) Pahang Branch between 2017 and 2021. The research procedure was divided into two parts: data collecting and pre-processing, and building the machine learning algorithm, pre-training and testing.

Findings

Gender, family income, region and achievement in the national secondary school examination (Sijil Pelajaran Malaysia [SPM]) predict academic performance. Female students were 1.2 times more likely to succeed academically. Central region students performed better with a value of 1.26. M40-income students were more likely to excel with an odds ratio of 2.809. Students who excelled in SPM English and Mathematics had a better likelihood of succeeding in higher education.

Research limitations/implications

This research was limited to pre-diploma students from UiTM Pahang Branch. For better generalizability of the results, future research should include pre-diploma students from other UiTM branches that offer this programme.

Practical implications

This study is expected to offer insights for policymakers, particularly, the Ministry of Higher Education, in developing a comprehensive policy to improve the tertiary education system by focusing on the fourth Sustainable Development Goal.

Social implications

These pre-diploma students were found to originate mainly from low- or middle-income families; hence, the programme may help them acquire better jobs and improve their standard of living. Most students enrolling on the pre-diploma performed below excellent at the secondary school level and were therefore given the opportunity to continue studying at a higher level.

Originality/value

This predictive model contributes to guidelines on the minimum requirements for pre-diploma students to gain admission into higher education institutions by ensuring the efficient distribution of resources and equal access to higher education among all communities.

Citation

Mohd Zaki, S., Razali, S., Awang Kader, M.A.R., Laton, M.Z., Ishak, M. and Mohd Burhan, N. (2024), "Predicting students’ performance at higher education institutions using a machine learning approach", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-12-2023-2742